Top 15 Tips To Make Big Data Applications
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Top 15 Tips To Make Big Data Applications

Hussain Fakhruddin - December 9, 2015 - 0 comments

The market for data-driven mobile applications is getting bigger – there are no two ways about it. By the end of this year, the global big data app market will be a $17 billion industry (as per IDC survey) – a mighty impressive figure, considering that the value of this market was only $3.3 billion in 2010. With most leading corporate houses across the world opting to extend operations with the help of such apps, developers are regularly taking up custom big data app development projects. In today’s discussion, we provide some essential tips on creating these big data apps:


  1. Prepare for revisions and iterations – Unlike general iPhone or Android apps, big data applications generally do not have any clear-cut objective to start out with. They are primarily created for data mining purposes – to collect and store customer information that MIGHT (and not SURELY) help in generating more business. As the app brings in more and more data, trends and insights become clearer, targets become more certain, and objectives can then be defined. At the time of conceptualizing and creating the prototype of big data mobile apps, developers need to be ready to make changes, as and when necessary. Flexibility is the name of the game here, and iterations with tweaks in the app are vital.
  2. Use real-time querying methods for data collection – Data-driven enterprise apps have to provide detailed, high-fidelity information to companies, and they need to perform this task quick. The reason for this emphasis on data mining speed is simple: the competition is getting hotter, and getting important customer data before competitors do can be a game-changer. In this scenario, mobile app developers invariably recommend the implementation of real-time query in big data applications. The data should flow in via an incremental, integrated and seamless manner. The app data is, more often than not, multi-structured – and they have to be managed efficiently and quickly by apps. That only can deliver competitive advantages.
  3. Never compromise on quality – True for any mobile app, and particularly critical for the ones with big data in their backend resources. The overall percentage of uninstallation of applications (on iOS and Android) due to bugs, speed problems and other performance issues stands at alarming 86% in 2015. With so many rival app companies jumping into the big data development bandwagon, there is hardly any room for making mistakes and hoping to rectify things later on. Do not ever be in a rush while working on a big data project. Apparently minor oversights can totally ruin your app.
  4. Decide how to monitor the app’s performance – Big data app development is iterative, and these iterations have to be based on the performance of the application (a circular process, if you will). Experts from the field of mobile app development have repeatedly highlighted the importance of using certain relevant Key Performance Indicators, or KPIs, for this purpose. The metrics in the KPI-set should be chosen in a manner that they continue delivering progressive reports on app performance for a long-time (a myopic approach would never work here). Depending on the precise nature of a big data app, the KPIs can include figures like revenue-per-installations and number of downloads-per-acquisition. The focus needs to be on enhancing the retention of big data apps.
  5. Be proactive during app testing – Yes, there are error logs and lists of previously seen viruses and other threats that can affect big data applications. However, testing your mobile app simply on the basis of these would be inadequate. The type of data that an application collects, and the way in which this data mining is done, can expose the app to new threats and issues. During the mobile app testing phase, take time out to jot down all the types of threats that your app might be exposed to (in addition to those mentioned in error logs), and check the software for each of them.
  6. Make the app data actionable – Purely descriptive information is great for writing research reports and thesis papers. For a mobile app driven by big data though, the information needs to be actionable. Make sure that your application generates such data that would indeed help businesses (or independent entrepreneurs, as the case may be) in strategy-making, marketing, and day-to-day operational purposes. In most cases, a big data app should also has the capacity to predict future trends on the basis of past data. Accuracy, hence, is of essence too.
  7. Kickstart with small databases – A large business house will potentially have hundreds of terabytes of information. Including all of it in the backend server database of a mobile app is a sureshot recipe of the latter’s failure. It is always a good idea to start off with a relatively small, manageable database – and then scale it up, if required. With the growth of Internet of Things (IoT) also on a fast track worldwide, the volume of available information will only get larger. App developers need to sit with clients and determine the size of data that should be present in the initial versions of the application. Apps with information overload often turn out to be messy and hardly of any use – and what’s more, their development costs are often exorbitant.
  8. Make use of cloud services – From shortening decision timelines and reducing capital expenses, to cutting down on important data security risks – cloud services offer a lot of advantages over traditional multi-structure databases on local app servers (such database maintenance can be financially draining too). Cloud services like Google Compute Engine and Microsoft Azure can also help in improving the scalability of big data applications (both upward and downward), on a very granular level. New datasets can be included with ease as well. The ‘pay-as-you-go’ cost models of data-driven applications with cloud support also make them popular among enterprise clients. They have to pay ONLY for whatever service they use.
  9. Use device hardware to capture data – Most of the latest flagship smartphones have 15-20 sensors. Most, if not all, can be used to capture data – data that is refined and obviously, collected first-hand. This is precisely why iPhone app development professionals harp on the need for any good big data app to work in sync with the various sensors present in the device hardware. Many existing business apps do not yet do this – and it is expected that compatibility with smartphone sensors will be a focus area of mobile developers in the foreseeable future.
  10. Relevant data matters, all data doesn’t – Why is a big data application even created in the first place? That’s right, to obtain pertinent, real-time information that would help in maximizing business growth potentials. There is absolutely no need for the final version of a big data app (the beta test versions can do this though) to collect gluttons of unsorted information – since only a fraction of it would be of actual use. What’s more, an app that neglects the relevancy factor while collecting data sacrifices on speed and efficiency, is more costly, and is more difficult to maintain as well. Find out from clients about the business metrics that your big data app needs to focus on, and develop accordingly.
  11. User-experience is still the most important factor – Data-driven mobile enterprise apps should never pose problems at the time of actual deployment. Remember, you can have as many fancy analytics features and data collection nodes in an app as you want – but it is the UI and ease-of-usage of the app that would determine its acceptability in an organization. Analytics are not, and shall never be, a substitute of user-experience. Another factor that app developers have to keep in mind is that big data applications are required to COMPLEMENT the existing knowledge pool of managers and entrepreneurs. The latter do not need to learn everything from scratch.
  12. The big data should help in formulating quick solutions – A successful big data app should have the capability to add ‘intelligence’ to the collected stats, analyze problems from a fresh perspective – and help users arrive at innovative, viable solutions. The customized information generated by these applications should be accessible to the decision-makers of organizations quickly and easily. Big data can go a long way in helping entrepreneurs think beyond static solutions, and your app needs to give them the right resources for that.
  13. Customization – On both the Android and the iOS platforms, mobile apps driven by big data need to have high-end customization features. Users of these applications might want, and often need to, access the data in different ways – to get the correct insights and understanding. The onus is on the app developers to make their app work beyond static hierarchical data systems, and create sandboxes and faceted search options. People who are in charge of taking decisions on the basis of information thrown up from big data apps are, in effect, ‘data scientists’ – and they need to be able to use your app in the way they want.
  14. One size most certainly does not fit all – Let us consider two business firms – Firm A and Firm B. The first is an advertising firm, and it is looking for an answer to the query – ‘What advertising campaigns should we launch for the next year?’ It needs a big-data app that provides ad-hoc information on past customer purchase behaviour, so that the right decision can be taken on its basis. Firm B, on the other hand, provides information services, and is looking for an application that would answer queries like ‘Which hotels near my house are currently accepting room reservations?’ In the data app for this firm, GPS-support and real-time data streaming are two must-have features (historical data is not of any use here). Understand the nature of business of an enterprise and get an idea of the problem(s) it is trying to solve with a big data application. That will help you in churning out the ‘right’ app.
  15. Automated test-driven development is the way to go – If a big data app has bugs or security concerns, its reviews will be poor, the client will walk away in a huff, and your mobile app agency will earn a bad reputation. With the total number of devices on which an app has to be compatible being large (and getting larger every quarter), automated quality assurance (QA) is the best possible method for dedicated app testing. This test-driven development process is meant to augment traditional manual beta testing – and can be of great help in quickly detecting bugs and errors, which can then be removed. Automated app tests act as a buffer over human errors, and also saves both time and money. A bug-free, properly functioning app – that’s what manual+automated testing promises.


Mobile app developers also need to include tools in big data apps for in-depth analysis and interpretation of the collected data. The core algorithms of such apps need to be sound, and users should have the chance to examine data from different unique perspectives. Be very careful while blocking out all possible data security threats (of which there are many) that your app might be susceptible to. The compound annual growth rate of big data applications will be almost 27% by 2018 – and developers need to create their apps with due care, to survive and thrive in this domain.



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